"""simple docstring"""



import argparse
import json

import numpy
import torch

from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()



def 	_lowerCamelCase(	a   ,    a				):
       # Load checkpoint
       __a       =    torch.load(a   ,    map_location="cpu"				)

       __a       =    chkpt["model"]

       # We have the base model one level deeper than the original XLM repository
       __a       =    {}
       for k, v in state_dict.items():
              if "pred_layer" in k:
                     __a       =    v
              else:
                     __a       =    v

       __a       =    chkpt["params"]
       __a       =    {n: v for n, v in config.items() if not isinstance(a   ,    (torch.FloatTensor, numpy.ndarray)				)}

       __a       =    chkpt["dico_word2id"]
       __a       =    {s + "</w>" if s.find("@@"				) == -1 and i > 1_3 else s.replace("@@"   ,    ""				): i for s, i in vocab.items()}

       # Save pytorch-model
       __a       =    pytorch_dump_folder_path + "/" + WEIGHTS_NAME
       __a       =    pytorch_dump_folder_path + "/" + CONFIG_NAME
       __a       =    pytorch_dump_folder_path + "/" + VOCAB_FILES_NAMES["vocab_file"]

       print(F"Save PyTorch model to {pytorch_weights_dump_path}"				)
       torch.save(a   ,    a				)

       print(F"Save configuration file to {pytorch_config_dump_path}"				)
       with open(a   ,    "w"   ,    encoding="utf-8"				) as f:
              f.write(json.dumps(a   ,    indent=2				) + "\n"				)

       print(F"Save vocab file to {pytorch_config_dump_path}"				)
       with open(a   ,    "w"   ,    encoding="utf-8"				) as f:
              f.write(json.dumps(a   ,    indent=2				) + "\n"				)


if __name__ == "__main__":
   SCREAMING_SNAKE_CASE__:Optional[int]									=			argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       """--xlm_checkpoint_path""", default=None, type=str, required=True, help="""Path the official PyTorch dump."""
   )
   parser.add_argument(
       """--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
   )
   SCREAMING_SNAKE_CASE__:Dict									=			parser.parse_args()
   convert_xlm_checkpoint_to_pytorch(args.xlm_checkpoint_path, args.pytorch_dump_folder_path)

"""simple docstring"""



from arguments import InitializationArguments

from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, HfArgumentParser


# Configuration
SCREAMING_SNAKE_CASE__:str									=			HfArgumentParser(InitializationArguments)
SCREAMING_SNAKE_CASE__:List[str]									=			parser.parse_args()

# Load codeparrot tokenizer trained for Python code tokenization
SCREAMING_SNAKE_CASE__:List[Any]									=			AutoTokenizer.from_pretrained(args.tokenizer_name)

# Config: "scale_attn_by_layer_idx" and "reorder_and_upcast_attn" are Mistral stability tweaks
SCREAMING_SNAKE_CASE__:Union[str, Any]									=			{
    """vocab_size""": len(tokenizer),
    """scale_attn_by_inverse_layer_idx""": True,
    """reorder_and_upcast_attn""": True,
}

# Load model config (GPT-2 large in this case)
SCREAMING_SNAKE_CASE__:Optional[int]									=			AutoConfig.from_pretrained(args.config_name, **config_kwargs)

# Initialize new model with config
SCREAMING_SNAKE_CASE__:Tuple									=			AutoModelForCausalLM.from_config(config)

# Save model to the hub
model.save_pretrained(args.model_name, push_to_hub=args.push_to_hub)
